Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems

Richardson, Peter; Flynn, Damian; Keane, Andrew

doi:10.1109/tpwrs.2011.2158247

Cited by 464 publications

(189 citation statements)

References 14 publications

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“…That is, when an EV is parked at home or on campus, it is charged with δ i charging rate until fully charged. In this paper, we call this scheme the "min time charging scheduling scheme" [21]. It is important to note that this scheme does not require interaction between customers and control entities such as the microgrid energy management system (EMS) and fleet operator, i.e., it is an uncontrolled charging scheme.…”

Section: Min Time Charging Scheduling Schemementioning

confidence: 99%

Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands

Yoon

Kang

2017

Energies

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Two of the most important technologies for future power systems to reduce greenhouse gas are electric vehicles (EVs) and renewable generation. When EVs become more common, the overall demand of electricity will significantly increase because EVs consume a large amount of electricity. Also, a daily load curve with EVs heavily depends on how much electricity EVs consume and when electricity is consumed. The microgrid is an important technology to promote renewable generation, and the increased demand and changed load curve should be considered in the microgrid planning stage to install robust and economical microgrids. In this paper, we propose an algorithm for microgrid planning with EV charging demand to find the most economical configuration through which to maximally utilize renewable generation. The algorithm uses a renewable generation-following EV charging scheme and HOMER. Through simulations, it is shown that the microgrid constructed by the proposed algorithm reduces the investment cost and CO 2 emission.

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Section: Min Time Charging Scheduling Schemementioning

confidence: 99%

Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands

Yoon

Kang

2017

Energies

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“…As private vehicles play an important role in commuting (89.4% in 2009 by the National Household Travel Survey [26]), they have a relatively short travel time (the average travel time is 43 min in Beijing [27]) and a relatively long parking time (the average parking time is 8.7 h at the workplace for an 8+ h work day [27]). For private EVs, the charging after working hours can be arranged in a load valley, such as midnight, to prevent peak boost on the regular load peak during the evening hours [7]. On weekends, EVs usually demand to charge as soon as they are plugged in and as fast as possible, which makes them uncontrollable.…”

Section: Scenario Descriptionmentioning

confidence: 99%

“…EV-dominant management contributes to the availability of departure, lowest energy cost and longest battery life of EVs [7]. Because there is a great difference between the demands of each EV, EV-dominant management may cause unpredictable load strikes, which will impair power quality and security in depart earlier than expected can get acceptable charging results.…”

Section: Introductionmentioning

confidence: 99%

Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

et al. 2016

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Abstract:With the ongoing large-scale implementation of electric vehicles (EVs), the exploration of a more flexible approach to maintain fair interaction between EVs and the power grid is urgently required. This paper presents an aggregator-based interactive charging management scheme adopting interruptible load (IL) pricing, in which the EV aggregator will respond to the load control command of the grid in an EV interactive mode. Charging managements are carried out according to battery state-of-charge and the EV departure time in EV charging stations. A power-altering charging (PAC) control method is proposed to dispatch the EVs charging fairly in a station and guarantee EV owners' preferences. The method does not require classical iterative procedures or heavy computations; furthermore, it is beneficial for EVs to depart earlier than expected for reasons beyond keeping homeostatic charging. The proposed scheme, which is tested to charge individual EVs well according to its preference, was implemented as part of an "EV Beijing" project. The proposed management scheme provides new insight into EV charging strategy and provides another choice to EV users.

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“…In [7], a multi-objective scheduling of EVs in a smart distribution system is proposed, so as to minimize the total operation cost and emissions. In [8], to improve the security and economics of the grid operation, a model concerning with optimal power flow, statistic characteristics of EVs, Temporal domain [6] (1) power losses (2) the cost of purchased energy [7] (1) the total operational costs and emissions [8] (1) power loss (2) adjustment frequency for power grid control equipment (3) the smoothness for the power daily load curve (4) EV owners' degree of satisfaction [9] (1) peak-valley difference Spatial domain [10] (1) system charging time (2) system charging capacity (3) dispatching charging load [11] (1) generation cost (2) network losses [12] (1) the utilization of existing networks Temporal and Spatial domain [13] (1) First level: the discharging cost of EV, charging station corresponding transformer loading (2) Second level: network Losses [14] (1) transmission system: fuel cost; the PM 2.5 emission of a thermal unit; the start-up and shut-down cost of thermal unit; charging cost; wind curtailment cost (2) distribution system: network Losses…”

Section: Introductionmentioning

confidence: 99%

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

Yang

Chen

et al. 2016

Energies

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Abstract:With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.

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Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems

Cited by 464 publications

References 14 publications

Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands

Economic Microgrid Planning Algorithm with Electric Vehicle Charging Demands

Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles

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